There are many applications in which it is beneficial to know the dimensions of a room. An obvious example is in sound reproduction, where the room has a very large influence on the sound that is experienced by the user. Knowing the shape and dimensions of the room provides important information that can be used to optimize the sound reproduction for that particular room. For example, knowing the room dimensions enables prediction of important acoustical properties of the room, such as its low-frequency room modes (causing resonances at specific frequencies, leading to an unpleasant “boomy” bass sound), early reflection patterns, reverberation time, etc. Knowing these acoustical properties enables processing of loudspeaker signals in such a way that the sound experience in the room is optimized. Furthermore, knowing the room dimensions enables provision of specific advice to the user of a multi-speaker system on how to best set up the loudspeaker system.
Also, outside sound reproduction, there are many applications that benefit from knowing the room dimensions, e.g. any application in which knowledge about the user's context is used to optimize a user experience.
Although it is of course possible to manually measure room dimensions and entering them into a device, this is cumbersome and often impractical.
Visual methods exist that are able to provide some indication of room lay-out. These are typically based on still or moving image cameras. However, although some information can be obtained by such approaches, they tend to be limited by the viewing angle of the camera and are hindered by objects that are blocking the camera's view as well as varying lighting conditions. In addition they often require additional or dedicated equipment (such as the camera) and may require specific positioning of the camera which can be inconvenient.
Another possibility for at least partially automated room dimension estimation is to determine estimates based on acoustic measurements in a room. This may be particularly attractive for sound rendering applications where the audio rendering system may also comprise functionality for estimating the room dimensions
Various methods for acoustic room dimension estimation are known but these tend to be suboptimal, and in particular tend to be cumbersome, complex, and/or inaccurate. For example, acoustic methods are known that can generate an estimation of room volume by measuring reverberation time. However, this only results in a coarse indication of the overall room size (e.g. small, medium, large) and is not able to provide estimates of the individual dimensions.
Hence, an improved approach for determining a room dimension would be advantageous and in particular an approach allowing increased flexibility, facilitated operation, reduced complexity, reduced resource consumption, improved estimation accuracy and/or improved performance would be advantageous.